Scientist, Quantitative Systems Pharmacology

Sanofi•Morristown, NJ

17h•Hybrid

About The Position

The Quantitative Systems Pharmacology (QSP) group of Sanofi is seeking to hire a highly talented modeling scientist to support its portfolio in early clinical development. The QSP group is part of Translational Informatics department within the Translational Medicine and Early Development organization of Sanofi. The individual will primarily be involved with hands-on quantitative systems pharmacology modeling activities, and he/she will be responsible to work with internal and external collaborators to ensure alignment with the strategic needs of the project team. The position will be based in Morristown, NJ. Join the engine of Sanofi’s mission — where deep immunoscience meets bold, AI-powered research. In R&D, you’ll drive breakthroughs that could turn the impossible into possible for millions.

Requirements

PhD in systems pharmacology/biology, computational biology, biomedical engineering, or a related field with a strong record of productivity demonstrated through publications and scientific presentations with at least 2 years of Postdoctoral experience in disease modeling/QSP.
Master’s degree with 4+ years of relevant industry experience.
Proficiency in mathematical modeling using differential equations.
Extensive experience in computational tools such as MATLAB, Julia and R (MATLAB preferred).

Nice To Haves

Experience in drug discovery/development a plus, especially in bioinformatics or pharmacometrics.
Knowledge of Immunology.
Excellent oral and written communication skills.
Ability to quickly learn pathophysiology in indications of interest, and treatment approaches in immunology.
Highly motivated, detail-oriented, and independent researcher.
Able to thrive in a collaborative, multi-disciplinary team environment.
Strong commitment to on-the-job-training.

Responsibilities

Function as part of a team to develop and apply multiscale computational models of complex diseases based on detailed biological mechanistic knowledge to support model-informed drug discovery & development.
Survey literature to gain understanding of critical physiological processes to be represented, and identify suitable mechanistic elements, data, parameters and assumptions to be included.
Identify and analyze suitable internal preclinical and clinical data to inform modeling activities.
Analyze simulation results and identify appropriate strategies to resolve issues pertaining to model performance and accuracy.
Maintain extensive documentation of model development process, and data analysis.
Maintain quantitative systems pharmacology expertise through comprehensive education.
Communicate modeling predictions to key stakeholders.